Work

Impact of Big Data Analytics on Emergency Department Efficiency in Saudi Ministry of Health Hospitals: A Retrospective Data Analysis

Year: 2025

Type: article

Abstract: Background: The integration of big data analytics in healthcare has become essential for enhancing operational performance, particularly within Emergency Departments (EDs), where efficiency improvements can significantly impact patient satisfaction and resource utilization. Aim: This study examines the impact of big data analytics on ED performance metrics within Saudi Arabia’s Ministry of Health (MOH) hospitals, with a focus on key performance indicators (KPIs) and the effectiveness of the Ada’a Health Program in optimizing ED operations. Methods: A retrospective observational study was conducted across 10 hospitals in five regions of Saudi Arabia. Data from 228,857 patient records were analyzed, alongside survey responses from 223 ED personnel. Statistical analyses, including paired t-tests, Pearson’s correlation, and multiple regression models, were used to evaluate improvements in KPIs and assess the program’s impact. Results: Significant improvements in all KPIs were observed following the implementation of the Adaa Health Program. Door-to-Doctor Time decreased from 28:26 to 25:13, Doctor-to-Decision Time from 1:18:22 to 1:03:50, Decision-to-Disposition Time from 36:37 to 20:13, and Door-to-Disposition Time from 2:22:02 to 1:48:44. Pearson’s correlation analysis indicated a strong relationship between Decision-to-Disposition Time and Doctor-to-Decision Time (r = 0.594), emphasizing the role of clinical decision-making in patient flow. Regression analysis further confirmed the program’s significant association with reduced wait times (p < 0.001). Conclusion: This study highlights the transformative impact of big data-driven decision-making in optimizing ED efficiency. The Ada’a Health Program has significantly improved patient flow, reduced congestion, and enhanced operational performance in Saudi MOH hospitals. These findings underscore the need for continued investment in big data analytics, updated predictive modeling, and workflow automation to sustain and further enhance ED efficiency. Future research should explore scalability across diverse healthcare settings and the long-term sustainability of such interventions. Plain Language Summary: This study examines how big data analytics can enhance the efficiency of Emergency Departments (EDs) in hospitals under Saudi Arabia’s Ministry of Health (MOH). By analyzing data from over 228,000 patient records across 10 hospitals, we assessed the impact of the Ada’a Health Program, a data-driven initiative designed to improve hospital performance. Specifically, we evaluated Key Performance Indicators (KPIs), including the time it takes for patients to see a doctor, receive a treatment decision, and complete their ED visit. The findings revealed significant reductions in patient wait times from 2019 to 2021. For example, the time from arrival to seeing a doctor decreased from 28 minutes to 25 minutes, while the total time spent in the ED was reduced by over 30 minutes, demonstrating more efficient patient flow. These improvements highlight how real-time data monitoring and workflow optimization have enhanced resource management, service quality, and patient satisfaction in emergency care settings. This study underscores the transformative power of big data in healthcare, showing how data-driven decision-making can streamline hospital operations, reduce congestion, and improve patient experiences. The results emphasize the importance of continued investment in big data analytics to sustain and expand these benefits across healthcare systems. Keywords: healthcare analytics, emergency department efficiency, operational performance Improvement, patient flow management, Ada’a Health Program

Source: Risk Management and Healthcare Policy

DOI: 10.2147/rmhp.s503744

Authors: Mohammed Senitan, Bandar Alzahrani

Institution: Saudi Electronic University

Language: English

Topic: Emergency and Acute Care Studies

Subfield: Emergency Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,836